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BDL.NET: Bayesian dictionary learning in Infer.NET

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Subtitle of host publicationProceedings of a meeting held 13-16 September 2016, Vietri sul Mare (Salerno), Italy
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781509007462
ISBN (Print)9781509007479
DOIs
DateAccepted/In press - 31 Jul 2016
DateE-pub ahead of print - 10 Nov 2016
DatePublished (current) - Dec 2016
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: 13 Sep 201616 Sep 2016

Publication series

NameProceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1551-2541

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
CountryItaly
CityVietri sul Mare, Salerno
Period13/09/1616/09/16

Abstract

We introduce and analyse a flexible and efficient implementation of Bayesian dictionary learning for sparse coding. By placing Gaussian-inverse-Gamma hierarchical priors on the coefficients, the model can automatically determine the required sparsity level for good reconstructions, whilst also automatically learning the noise level in the data, obviating the need for heuristic methods for choosing sparsity levels. This model can be solved efficiently using Variational Message Passing (VMP), which we have implemented in the Infer.NET framework for probabilistic programming and inference. We analyse the properties of the model via empirical validation on several accelerometer datasets. We provide source code to replicate all of the experiments in this paper.

    Research areas

  • Accelerometers, Bayesian, Dictionary Learning, Sparse Coding

    Structured keywords

  • Jean Golding

Event

26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings

Duration13 Sep 201616 Sep 2016
CityVietri sul Mare, Salerno
CountryItaly
SponsorsIEEE Signal Processing Society (External organisation)

Event: Conference

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Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via IEEE at https://doi.org/10.1109/MLSP.2016.7738851 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 820 KB, PDF document

DOI

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